def main():
stateDim = env.observation_space.shape[0]
actionDim = env.action_space.n
buildModel = BuildModel(stateDim, actionDim)
layersWidths = [30]
writer, model = buildModel(layersWidths)
dirName = os.path.dirname(__file__)
modelPath = os.path.join(
dirName, '..', 'trainedDQNModels',
'epsilonIncrease=0.0002_epsilonMin=0_gamma=0.9_learningRate=0.001_maxEpisode=1000_maxTimeStep=2000_minibatchSize=128.ckpt'
)
restoreVariables(model, modelPath)
policy = lambda state: [ActGreedyByModel(getTrainQValue, model)(state)]
isTerminal = IsTerminalMountCarDiscrete()
transit = TransitMountCarDiscrete()
getReward = rewardMountCarDiscrete
reset = ResetMountCarDiscrete(seed=None, low=-1, high=0.4)
for i in range(20):
maxRunningSteps = 2000
sampleTrajectory = SampleTrajectory(maxRunningSteps, transit,
isTerminal, getReward, reset)
trajectory = sampleTrajectory(policy)
# plots& plot
showDemo = True
if showDemo:
visualize = VisualizeMountCarDiscrete()
visualize(trajectory)
def main():
stateDim = env.observation_space.shape[0]
actionDim = env.action_space.shape[0]
actionHigh = env.action_space.high
actionLow = env.action_space.low
actionBound = (actionHigh - actionLow) / 2
buildActorModel = BuildActorModel(stateDim, actionDim, actionBound)
actorLayerWidths = [30]
actorWriter, actorModel = buildActorModel(actorLayerWidths)
dirName = os.path.dirname(__file__)
actorModelPath = os.path.join(
dirName, '..', 'trainedDDPGModels',
'Eps=300_High=0.4_actorModel=0_batch=128_env=MountainCarContinuous-v0_gam=0.9_lrActor=0.001_lrCritic=0.001_noiseVar=1_resetLow=-1_timeStep=2000_varDiscout=0.99995.ckpt'
)
restoreVariables(actorModel, actorModelPath)
policy = ActDDPGOneStep(actionLow,
actionHigh,
actByPolicyTrain,
actorModel,
getNoise=None)
isTerminal = IsTerminalMountCarContin()
reset = ResetMountCarContin(seed=None, low=-1, high=0.4)
transit = TransitGymMountCarContinuous()
rewardFunc = RewardMountCarContin(isTerminal)
for i in range(20):
maxRunningSteps = 2000
sampleTrajectory = SampleTrajectory(maxRunningSteps, transit,
isTerminal, rewardFunc, reset)
trajectory = sampleTrajectory(policy)
# plots& plot
showDemo = True
if showDemo:
visualize = VisualizeMountCarContin()
visualize(trajectory)
def main():
stateDim = env.observation_space.shape[0]
actionDim = env.action_space.shape[0]
actionHigh = env.action_space.high
actionLow = env.action_space.low
actionBound = (actionHigh - actionLow) / 2
buildActorModel = BuildActorModel(stateDim, actionDim, actionBound)
actorLayerWidths = [30]
actorWriter, actorModel = buildActorModel(actorLayerWidths)
actorModelPath = os.path.join(
dirName, '..', 'trainedDDPGModels',
'Eps=200_actorModel=0_batch=128_env=Pendulum-v0_gam=0.9_lrActor=0.001_lrCritic=0.001_noiseVar=3_timeStep=200_varDiscout=0.9995.ckpt'
)
restoreVariables(actorModel, actorModelPath)
actOneStep = ActDDPGOneStep(actionLow,
actionHigh,
actByPolicyTrain,
actorModel,
getNoise=None)
policy = lambda state: actOneStep(observe(state))
isTerminal = isTerminalGymPendulum
reset = ResetGymPendulum(seed)
transit = TransitGymPendulum()
rewardFunc = RewardGymPendulum(angle_normalize)
for i in range(10):
maxRunningSteps = 200
sampleTrajectory = SampleTrajectory(maxRunningSteps, transit,
isTerminal, rewardFunc, reset)
trajectory = sampleTrajectory(policy)
# plots& plot
showDemo = True
if showDemo:
visualize = VisualizeGymPendulum()
visualize(trajectory)
def generateSingleCondition(condition):
debug = 0
if debug:
damping=2.0
frictionloss=0.0
masterForce=1.0
numWolves = 1
numSheeps = 1
numMasters = 1
maxTimeStep = 25
saveTraj=False
saveImage=True
visualizeMujoco=False
visualizeTraj = True
makeVideo=True
else:
# print(sys.argv)
# condition = json.loads(sys.argv[1])
damping = float(condition['damping'])
frictionloss = float(condition['frictionloss'])
masterForce = float(condition['masterForce'])
numWolves = 1
numSheeps = 1
numMasters = 1
maxTimeStep = 25
saveTraj=False
saveImage=True
visualizeMujoco=False
visualizeTraj = True
makeVideo=False
print("maddpg: , saveTraj: {}, visualize: {},damping; {},frictionloss: {}".format( str(saveTraj), str(visualizeMujoco),damping,frictionloss))
numAgents = numWolves + numSheeps+numMasters
numEntities = numAgents + numMasters
wolvesID = [0]
sheepsID = [1]
masterID = [2]
wolfSize = 0.075
sheepSize = 0.05
masterSize = 0.075
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheeps + [masterSize] * numMasters
wolfMaxSpeed = 1.0
blockMaxSpeed = None
entitiesMovableList = [True] * numAgents + [False] * numMasters
massList = [1.0] * numEntities
isCollision = IsCollision(getPosFromAgentState)
punishForOutOfBound = PunishForOutOfBound()
rewardSheep = RewardSheep(wolvesID, sheepsID, entitiesSizeList, getPosFromAgentState, isCollision,punishForOutOfBound)
rewardWolf = RewardWolf(wolvesID, sheepsID, entitiesSizeList, isCollision)
rewardMaster= lambda state, action, nextState: [-reward for reward in rewardWolf(state, action, nextState)]
rewardFunc = lambda state, action, nextState: \
list(rewardWolf(state, action, nextState)) + list(rewardSheep(state, action, nextState))+list(rewardMaster(state, action, nextState))
dirName = os.path.dirname(__file__)
# physicsDynamicsPath=os.path.join(dirName,'..','..','environment','mujocoEnv','rope','leased.xml')
makePropertyList=MakePropertyList(transferNumberListToStr)
geomIds=[1,2,3]
keyNameList=[0,1]
valueList=[[damping,damping]]*len(geomIds)
dampngParameter=makePropertyList(geomIds,keyNameList,valueList)
changeJointDampingProperty=lambda envDict,geomPropertyDict:changeJointProperty(envDict,geomPropertyDict,'@damping')
geomIds=[1,2,3]
keyNameList=[0,1]
valueList=[[frictionloss,frictionloss]]*len(geomIds)
frictionlossParameter=makePropertyList(geomIds,keyNameList,valueList)
changeJointFrictionlossProperty=lambda envDict,geomPropertyDict:changeJointProperty(envDict,geomPropertyDict,'@frictionloss')
physicsDynamicsPath=os.path.join(dirName,'..','..','environment','mujocoEnv','rope','leasedNew.xml')
# physicsDynamicsPath=os.path.join(dirName,'..','..','environment','mujocoEnv','rope','leased.xml')
with open(physicsDynamicsPath) as f:
xml_string = f.read()
envXmlDict = xmltodict.parse(xml_string.strip())
envXmlDict = xmltodict.parse(xml_string.strip())
envXmlPropertyDictList=[dampngParameter,frictionlossParameter]
changeEnvXmlPropertFuntionyList=[changeJointDampingProperty,changeJointFrictionlossProperty]
for propertyDict,changeXmlProperty in zip(envXmlPropertyDictList,changeEnvXmlPropertFuntionyList):
envXmlDict=changeXmlProperty(envXmlDict,propertyDict)
envXml=xmltodict.unparse(envXmlDict)
physicsModel = mujoco.load_model_from_xml(envXml)
physicsSimulation = mujoco.MjSim(physicsModel)
# print(physicsSimulation.model.body_pos)
# print(dir(physicsSimulation.model))
# print(dir(physicsSimulation.data),physicsSimulation.dataphysicsSimulation.data)
# print(physicsSimulation.data.qpos,dir(physicsSimulation.data.qpos))
# print(physicsSimulation.data.qpos,dir(physicsSimulation.data.qpos))
qPosInit = (0, ) * 24
qVelInit = (0, ) * 24
qPosInitNoise = 0.4
qVelInitNoise = 0
numAgent = 2
tiedAgentId = [0, 2]
ropePartIndex = list(range(3, 12))
maxRopePartLength = 0.06
reset = ResetUniformWithoutXPosForLeashed(physicsSimulation, qPosInit, qVelInit, numAgent, tiedAgentId,ropePartIndex, maxRopePartLength, qPosInitNoise, qVelInitNoise)
numSimulationFrames=10
isTerminal= lambda state: False
reshapeActionList = [ReshapeAction(5),ReshapeAction(5),ReshapeAction(masterForce)]
transit=TransitionFunctionWithoutXPos(physicsSimulation, numSimulationFrames, visualizeMujoco,isTerminal, reshapeActionList)
# damping=2.5
# numSimulationFrames =int(0.1/dt)
# agentsMaxSpeedList = [wolfMaxSpeed]* numWolves + [sheepMaxSpeed] * numSheeps
# reshapeAction = ReshapeAction()
# isTerminal = lambda state: False
# transit = TransitionFunctionWithoutXPos(physicsSimulation,numAgents , numSimulationFrames,damping*dt*numSimulationFrames,agentsMaxSpeedList,visualizeMujoco,isTerminal,reshapeAction)
maxRunningStepsToSample = 100
sampleTrajectory = SampleTrajectory(maxRunningStepsToSample, transit, isTerminal, rewardFunc, reset)
observeOneAgent = lambda agentID: Observe(agentID, wolvesID, sheepsID, masterID, getPosFromAgentState, getVelFromAgentState)
observe = lambda state: [observeOneAgent(agentID)(state) for agentID in range(numAgents)]
initObsForParams = observe(reset())
obsShape = [initObsForParams[obsID].shape[0] for obsID in range(len(initObsForParams))]
worldDim = 2
actionDim = worldDim * 2 + 1
layerWidth = [128, 128]
# ------------ model ------------------------
buildMADDPGModels = BuildMADDPGModels(actionDim, numAgents, obsShape)
modelsList = [buildMADDPGModels(layerWidth, agentID) for agentID in range(numAgents)]
# individStr = 'individ' if individualRewardWolf else 'shared'
# fileName = "maddpg{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}{}_agent".format(
# numWolves, numSheeps, numMasters, maxEpisode, maxTimeStep, sheepSpeedMultiplier, individStr)
# wolvesModelPaths = [os.path.join(dirName, '..', 'trainedModels', '3wolvesMaddpg', fileName + str(i) + '60000eps') for i in wolvesID]
# [restoreVariables(model, path) for model, path in zip(wolvesModel, wolvesModelPaths)]
#
# actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
# policy = lambda allAgentsStates: [actOneStepOneModel(model, observe(allAgentsStates)) for model in modelsList]
# modelPaths = [os.path.join(dirName, '..', 'trainedModels', '3wolvesMaddpg_ExpEpsLengthAndSheepSpeed', fileName + str(i)) for i in
# range(numAgents)]
# modelFolder = os.path.join(dirName, '..', 'trainedModels', 'mujocoMADDPG')
# modelFolder = os.path.join(dirName, '..', 'trainedModels', 'mujocoMADDPG','timeconst='+str(timeconst)+'dampratio='+str(dampratio))
modelFolder = os.path.join(dirName, '..', 'trainedModels', 'mujocoMADDPGLeasedFixedEnv2','damping={}_frictionloss={}_masterForce={}'.format(damping,frictionloss,masterForce))
fileName = "maddpg{}episodes{}step_agent".format(maxEpisode, maxTimeStep)
modelPaths = [os.path.join(modelFolder, fileName + str(i) + '60000eps') for i in range(numAgents)]
[restoreVariables(model, path) for model, path in zip(modelsList, modelPaths)]
actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
policy = lambda allAgentsStates: [actOneStepOneModel(model, observe(allAgentsStates)) for model in modelsList]
trajList = []
numTrajToSample = 5
for i in range(numTrajToSample):
np.random.seed(i)
traj = sampleTrajectory(policy)
trajList.append(list(traj))
# saveTraj
if saveTraj:
trajFileName = "maddpg{}wolves{}sheep{}blocks{}eps{}stepSheepSpeed{}{}Traj".format(numWolves, numSheeps, numMasters, maxEpisode, maxTimeStep, sheepSpeedMultiplier, individStr)
trajSavePath = os.path.join(dirName, '..', 'trajectory', trajFileName)
saveToPickle(trajList, trajSavePath)
# visualize
if visualizeTraj:
demoFolder = os.path.join(dirName, '..', 'demos', 'mujocoMADDPGLeasedFixedEnv2','damping={}_frictionloss={}_masterForce={}'.format(damping,frictionloss,masterForce))
if not os.path.exists(demoFolder):
os.makedirs(demoFolder)
entitiesColorList = [wolfColor] * numWolves + [sheepColor] * numSheeps + [masterColor] * numMasters
render = Render(entitiesSizeList, entitiesColorList, numAgents,demoFolder,saveImage, getPosFromAgentState)
# print(trajList[0][0],'!!!3',trajList[0][1])
trajToRender = np.concatenate(trajList)
render(trajToRender)
def main():
numAgents = 2
stateDim = numAgents * 2
actionLow = -1
actionHigh = 1
actionBound = (actionHigh - actionLow) / 2
actionDim = 2
buildActorModel = BuildActorModel(stateDim, actionDim, actionBound)
actorLayerWidths = [64]
actorWriter, actorModel = buildActorModel(actorLayerWidths)
dirName = os.path.dirname(__file__)
actorModelPath = os.path.join(
dirName, '..', 'trainedDDPGModels',
'actorModel=0_dimension=2_gamma=0.95_learningRateActor=0.001_learningRateCritic=0.001_maxEpisode=2000_maxTimeStep=20_minibatchSize=32_wolfSpeed=0.5.ckpt'
)
# 'actorModel=0_dimension=2_gamma=0.95_learningRateActor=0.01_learningRateCritic=0.01_maxEpisode=400_maxTimeStep=100_minibatchSize=32_wolfSpeed=1.ckpt')
# 'actorModel=0_dimension=2_gamma=0.95_learningRateActor=0.01_learningRateCritic=0.01_maxEpisode=1000_maxTimeStep=100_minibatchSize=32_wolfSpeed=0.5.ckpt')
# 'actorModel=0_dimension=2_gamma=0.95_learningRateActor=0.01_learningRateCritic=0.01_maxEpisode=5000_maxTimeStep=100_minibatchSize=32_wolfSpeed=0.5.ckpt')
restoreVariables(actorModel, actorModelPath)
sheepPolicy = ActDDPGOneStep(actionLow,
actionHigh,
actByPolicyTrain,
actorModel,
getNoise=None)
sheepId = 0
wolfId = 1
getSheepPos = GetAgentPosFromState(sheepId)
getWolfPos = GetAgentPosFromState(wolfId)
wolfSpeed = 0.5
wolfPolicy = HeatSeekingContinuousDeterministicPolicy(
getWolfPos, getSheepPos, wolfSpeed)
xBoundary = (0, 20)
yBoundary = (0, 20)
stayWithinBoundary = StayWithinBoundary(xBoundary, yBoundary)
transit = TransitForNoPhysics(getIntendedNextState, stayWithinBoundary)
# transit = TransitWithSingleWolf(physicalTransition, wolfPolicy)
maxTimeStep = 20
sheepAliveBonus = 1 / maxTimeStep
sheepTerminalPenalty = 20
killzoneRadius = 0
isTerminal = IsTerminal(getWolfPos, getSheepPos, killzoneRadius)
getBoundaryPunishment = GetBoundaryPunishment(xBoundary,
yBoundary,
sheepIndex=0,
punishmentVal=10)
rewardSheep = RewardFunctionCompete(sheepAliveBonus, sheepTerminalPenalty,
isTerminal)
rewardSheepWithBoundaryHeuristics = RewardSheepWithBoundaryHeuristics(
rewardSheep, getIntendedNextState, getBoundaryPunishment, getSheepPos)
getSheepAction = lambda actions: [
actions[sheepId * actionDim], actions[sheepId * actionDim + 1]
]
getReward = lambda state, action, nextState: rewardSheepWithBoundaryHeuristics(
state, getSheepAction(action), nextState)
policy = lambda state: list(sheepPolicy(state)) + list(wolfPolicy(state))
# reset = Reset(xBoundary, yBoundary, numAgents)
reset = lambda: np.array([10, 10, 15, 5])
for i in range(10):
maxRunningSteps = 50
sampleTrajectory = SampleTrajectory(maxRunningSteps, transit,
isTerminal, getReward, reset)
trajectory = sampleTrajectory(policy)
# plots& plot
showDemo = True
if showDemo:
observe = Observe(trajectory, numAgents)
fullScreen = False
screenWidth = 800
screenHeight = 800
screen = initializeScreen(fullScreen, screenWidth, screenHeight)
leaveEdgeSpace = 200
lineWidth = 3
xBoundary = [leaveEdgeSpace, screenWidth - leaveEdgeSpace * 2]
yBoundary = [leaveEdgeSpace, screenHeight - leaveEdgeSpace * 2]
screenColor = THECOLORS['black']
lineColor = THECOLORS['white']
drawBackground = DrawBackground(screen, screenColor, xBoundary,
yBoundary, lineColor, lineWidth)
circleSize = 10
positionIndex = [0, 1]
drawState = DrawState(screen, circleSize, positionIndex,
drawBackground)
numberOfAgents = 2
chasingColors = [THECOLORS['green'], THECOLORS['red']]
colorSpace = chasingColors[:numberOfAgents]
FPS = 60
chaseTrial = ChaseTrialWithTraj(FPS,
colorSpace,
drawState,
saveImage=True)
rawXRange = [0, 20]
rawYRange = [0, 20]
scaledXRange = [210, 590]
scaledYRange = [210, 590]
scaleTrajectory = ScaleTrajectory(positionIndex, rawXRange,
rawYRange, scaledXRange,
scaledYRange)
oldFPS = 5
adjustFPS = AdjustDfFPStoTraj(oldFPS, FPS)
getTrajectory = lambda rawTrajectory: scaleTrajectory(
adjustFPS(rawTrajectory))
positionList = [observe(index) for index in range(len(trajectory))]
positionListToDraw = getTrajectory(positionList)
currentDir = os.getcwd()
parentDir = os.path.abspath(os.path.join(currentDir, os.pardir))
imageFolderName = 'Demo'
saveImageDir = os.path.join(os.path.join(parentDir, 'chasingDemo'),
imageFolderName)
if not os.path.exists(saveImageDir):
os.makedirs(saveImageDir)
chaseTrial(numberOfAgents, positionListToDraw, saveImageDir)
def main():
debug = 1
if debug:
numWolves = 1
numSheeps = 1
numBlocks = 1
saveTraj = False
visualizeTraj = True
maxTimeStep = 25
sheepSpeedMultiplier = 1
individualRewardWolf = 0
timeconst=0.5
dampratio=0.2
else:
print(sys.argv)
condition = json.loads(sys.argv[1])
numWolves = int(condition['numWolves'])
numSheeps = int(condition['numSheeps'])
numBlocks = int(condition['numBlocks'])
saveTraj = True
visualizeTraj = False
maxTimeStep = 50
sheepSpeedMultiplier = 1.25
individualRewardWolf = 1
print("maddpg: {} wolves, {} sheep, {} blocks, saveTraj: {}, visualize: {}".format(numWolves, numSheeps, numBlocks, str(saveTraj), str(visualizeTraj)))
numAgents = numWolves + numSheeps
numEntities = numAgents + numBlocks
wolvesID = list(range(numWolves))
sheepsID = list(range(numWolves, numAgents))
blocksID = list(range(numAgents, numEntities))
wolfSize = 0.075
sheepSize = 0.05
blockSize = 0.2
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheeps + [blockSize] * numBlocks
wolfMaxSpeed = 1.0
blockMaxSpeed = None
sheepMaxSpeedOriginal = 1.3
sheepMaxSpeed = sheepMaxSpeedOriginal * sheepSpeedMultiplier
entityMaxSpeedList = [wolfMaxSpeed] * numWolves + [sheepMaxSpeed] * numSheeps + [blockMaxSpeed] * numBlocks
entitiesMovableList = [True] * numAgents + [False] * numBlocks
massList = [1.0] * numEntities
isCollision = IsCollision(getPosFromAgentState)
punishForOutOfBound = PunishForOutOfBound()
rewardSheep = RewardSheep(wolvesID, sheepsID, entitiesSizeList, getPosFromAgentState, isCollision,
punishForOutOfBound)
if individualRewardWolf:
rewardWolf = RewardWolfIndividual(wolvesID, sheepsID, entitiesSizeList, isCollision)
else:
rewardWolf = RewardWolf(wolvesID, sheepsID, entitiesSizeList, isCollision)
rewardFunc = lambda state, action, nextState: list(rewardWolf(state, action, nextState)) + list(rewardSheep(state, action, nextState))
reset = ResetMultiAgentChasing(numAgents, numBlocks)
observeOneAgent = lambda agentID: Observe(agentID, wolvesID, sheepsID, blocksID, getPosFromAgentState, getVelFromAgentState)
observe = lambda state: [observeOneAgent(agentID)(state) for agentID in range(numAgents)]
reshapeAction = ReshapeAction()
getCollisionForce = GetCollisionForce()
applyActionForce = ApplyActionForce(wolvesID, sheepsID, entitiesMovableList)
applyEnvironForce = ApplyEnvironForce(numEntities, entitiesMovableList, entitiesSizeList,
getCollisionForce, getPosFromAgentState)
integrateState = IntegrateState(numEntities, entitiesMovableList, massList,
entityMaxSpeedList, getVelFromAgentState, getPosFromAgentState)
transit = TransitMultiAgentChasing(numEntities, reshapeAction, applyActionForce, applyEnvironForce, integrateState)
isTerminal = lambda state: False
maxRunningStepsToSample = 100
sampleTrajectory = SampleTrajectory(maxRunningStepsToSample, transit, isTerminal, rewardFunc, reset)
initObsForParams = observe(reset())
obsShape = [initObsForParams[obsID].shape[0] for obsID in range(len(initObsForParams))]
worldDim = 2
actionDim = worldDim * 2 + 1
layerWidth = [128, 128]
# ------------ model ------------------------
buildMADDPGModels = BuildMADDPGModels(actionDim, numAgents, obsShape)
modelsList = [buildMADDPGModels(layerWidth, agentID) for agentID in range(numAgents)]
dirName = os.path.dirname(__file__)
# individStr = 'individ' if individualRewardWolf else 'shared'
# fileName = "maddpg{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}{}_agent".format(
# numWolves, numSheeps, numBlocks, maxEpisode, maxTimeStep, sheepSpeedMultiplier, individStr)
fileName = "maddpg{}wolves{}sheep{}blocks60000episodes25stepSheepSpeed1shared_agent".format(numWolves, numSheeps, numBlocks)
# wolvesModelPaths = [os.path.join(dirName, '..', 'trainedModels', '3wolvesMaddpg', fileName + str(i) + '60000eps') for i in wolvesID]
# [restoreVariables(model, path) for model, path in zip(wolvesModel, wolvesModelPaths)]
#
# actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
# policy = lambda allAgentsStates: [actOneStepOneModel(model, observe(allAgentsStates)) for model in modelsList]
# modelPaths = [os.path.join(dirName, '..', 'trainedModels', '3wolvesMaddpg_ExpEpsLengthAndSheepSpeed', fileName + str(i)) for i in
# range(numAgents)]
modelFolder = os.path.join(dirName, '..', 'trainedModels', 'mujocoMADDPG','timeconst='+str(timeconst)+'dampratio='+str(dampratio))
modelPaths = [os.path.join(modelFolder, fileName + str(i) + '60000eps') for i in range(numAgents)]
[restoreVariables(model, path) for model, path in zip(modelsList, modelPaths)]
actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
policy = lambda allAgentsStates: [actOneStepOneModel(model, observe(allAgentsStates)) for model in modelsList]
trajList = []
numTrajToSample = 50
for i in range(numTrajToSample):
traj = sampleTrajectory(policy)
trajList.append(list(traj))
# saveTraj
if saveTraj:
trajFileName = "maddpg{}wolves{}sheep{}blocks{}eps{}stepSheepSpeed{}{}Traj".format(numWolves, numSheeps, numBlocks, maxEpisode, maxTimeStep, sheepSpeedMultiplier, individStr)
trajSavePath = os.path.join(dirName, '..', 'trajectory', trajFileName)
saveToPickle(trajList, trajSavePath)
# visualize
if visualizeTraj:
entitiesColorList = [wolfColor] * numWolves + [sheepColor] * numSheeps + [blockColor] * numBlocks
render = Render(entitiesSizeList, entitiesColorList, numAgents, getPosFromAgentState)
trajToRender = np.concatenate(trajList)
render(trajToRender)
def main():
numTrajToSample = 500
maxRunningStepsToSample = 50
wolfSize = 0.075
sheepSize = 0.05
blockSize = 0.2
sheepMaxSpeed = 1.3
wolfMaxSpeed = 1.0
blockMaxSpeed = None
numWolves = 3
numBlocks = 2
maxEpisode = 60000
numSheepsList = [1, 2, 4, 8]
meanRewardList = []
seList = []
for numSheeps in numSheepsList:
numAgents = numWolves + numSheeps
numEntities = numAgents + numBlocks
wolvesID = list(range(numWolves))
sheepsID = list(range(numWolves, numAgents))
blocksID = list(range(numAgents, numEntities))
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheeps + [
blockSize
] * numBlocks
entityMaxSpeedList = [wolfMaxSpeed] * numWolves + [
sheepMaxSpeed
] * numSheeps + [blockMaxSpeed] * numBlocks
entitiesMovableList = [True] * numAgents + [False] * numBlocks
massList = [1.0] * numEntities
isCollision = IsCollision(getPosFromAgentState)
rewardWolf = RewardWolf(wolvesID, sheepsID, entitiesSizeList,
isCollision)
punishForOutOfBound = PunishForOutOfBound()
rewardSheep = RewardSheep(wolvesID, sheepsID, entitiesSizeList,
getPosFromAgentState, isCollision,
punishForOutOfBound)
rewardFunc = lambda state, action, nextState: \
list(rewardWolf(state, action, nextState)) + list(rewardSheep(state, action, nextState))
reset = ResetMultiAgentChasing(numAgents, numBlocks)
observeOneAgent = lambda agentID: Observe(
agentID, wolvesID, sheepsID, blocksID, getPosFromAgentState,
getVelFromAgentState)
observe = lambda state: [
observeOneAgent(agentID)(state) for agentID in range(numAgents)
]
reshapeAction = ReshapeAction()
getCollisionForce = GetCollisionForce()
applyActionForce = ApplyActionForce(wolvesID, sheepsID,
entitiesMovableList)
applyEnvironForce = ApplyEnvironForce(numEntities, entitiesMovableList,
entitiesSizeList,
getCollisionForce,
getPosFromAgentState)
integrateState = IntegrateState(numEntities, entitiesMovableList,
massList, entityMaxSpeedList,
getVelFromAgentState,
getPosFromAgentState)
transit = TransitMultiAgentChasing(numEntities, reshapeAction,
applyActionForce, applyEnvironForce,
integrateState)
isTerminal = lambda state: False
sampleTrajectory = SampleTrajectory(maxRunningStepsToSample, transit,
isTerminal, rewardFunc, reset)
initObsForParams = observe(reset())
obsShape = [
initObsForParams[obsID].shape[0]
for obsID in range(len(initObsForParams))
]
worldDim = 2
actionDim = worldDim * 2 + 1
layerWidth = [128, 128]
buildMADDPGModels = BuildMADDPGModels(actionDim, numAgents, obsShape)
modelsList = [
buildMADDPGModels(layerWidth, agentID)
for agentID in range(numAgents)
]
dirName = os.path.dirname(__file__)
fileName = "maddpg{}wolves{}sheep{}blocks{}eps_agent".format(
numWolves, numSheeps, numBlocks, maxEpisode)
modelPaths = [
os.path.join(dirName, '..', 'trainedModels', '3wolvesMaddpg',
fileName + str(i) + '60000eps')
for i in range(numAgents)
]
[
restoreVariables(model, path)
for model, path in zip(modelsList, modelPaths)
]
actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
policy = lambda allAgentsStates: [
actOneStepOneModel(model, observe(allAgentsStates))
for model in modelsList
]
trajRewardList = []
for i in range(numTrajToSample):
traj = sampleTrajectory(policy)
trajReward = calcTrajRewardWithSharedWolfReward(traj)
trajRewardList.append(trajReward)
meanTrajReward = np.mean(trajRewardList)
seTrajReward = np.std(trajRewardList) / np.sqrt(
len(trajRewardList) - 1)
print('meanTrajReward: ', meanTrajReward)
meanRewardList.append(meanTrajReward)
seList.append(seTrajReward)
# ---- individually rewarded wolves ------
meanRewardListIndividWolf = []
seListIndividWolf = []
for numSheeps in numSheepsList:
numAgents = numWolves + numSheeps
numEntities = numAgents + numBlocks
wolvesID = list(range(numWolves))
sheepsID = list(range(numWolves, numAgents))
blocksID = list(range(numAgents, numEntities))
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheeps + [
blockSize
] * numBlocks
entityMaxSpeedList = [wolfMaxSpeed] * numWolves + [
sheepMaxSpeed
] * numSheeps + [blockMaxSpeed] * numBlocks
entitiesMovableList = [True] * numAgents + [False] * numBlocks
massList = [1.0] * numEntities
isCollision = IsCollision(getPosFromAgentState)
rewardWolf = RewardWolfIndividual(wolvesID, sheepsID, entitiesSizeList,
isCollision)
punishForOutOfBound = PunishForOutOfBound()
rewardSheep = RewardSheep(wolvesID, sheepsID, entitiesSizeList,
getPosFromAgentState, isCollision,
punishForOutOfBound)
rewardFunc = lambda state, action, nextState: \
list(rewardWolf(state, action, nextState)) + list(rewardSheep(state, action, nextState))
reset = ResetMultiAgentChasing(numAgents, numBlocks)
observeOneAgent = lambda agentID: Observe(
agentID, wolvesID, sheepsID, blocksID, getPosFromAgentState,
getVelFromAgentState)
observe = lambda state: [
observeOneAgent(agentID)(state) for agentID in range(numAgents)
]
reshapeAction = ReshapeAction()
getCollisionForce = GetCollisionForce()
applyActionForce = ApplyActionForce(wolvesID, sheepsID,
entitiesMovableList)
applyEnvironForce = ApplyEnvironForce(numEntities, entitiesMovableList,
entitiesSizeList,
getCollisionForce,
getPosFromAgentState)
integrateState = IntegrateState(numEntities, entitiesMovableList,
massList, entityMaxSpeedList,
getVelFromAgentState,
getPosFromAgentState)
transit = TransitMultiAgentChasing(numEntities, reshapeAction,
applyActionForce, applyEnvironForce,
integrateState)
isTerminal = lambda state: False
sampleTrajectory = SampleTrajectory(maxRunningStepsToSample, transit,
isTerminal, rewardFunc, reset)
initObsForParams = observe(reset())
obsShape = [
initObsForParams[obsID].shape[0]
for obsID in range(len(initObsForParams))
]
worldDim = 2
actionDim = worldDim * 2 + 1
layerWidth = [128, 128]
buildMADDPGModels = BuildMADDPGModels(actionDim, numAgents, obsShape)
modelsList = [
buildMADDPGModels(layerWidth, agentID)
for agentID in range(numAgents)
]
dirName = os.path.dirname(__file__)
fileName = "maddpgIndividWolf{}wolves{}sheep{}blocks{}eps_agent".format(
numWolves, numSheeps, numBlocks, maxEpisode)
modelPaths = [
os.path.join(dirName, '..', 'trainedModels', '3wolvesMaddpg',
fileName + str(i) + '60000eps')
for i in range(numAgents)
]
[
restoreVariables(model, path)
for model, path in zip(modelsList, modelPaths)
]
actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
policy = lambda allAgentsStates: [
actOneStepOneModel(model, observe(allAgentsStates))
for model in modelsList
]
trajRewardList = []
for i in range(numTrajToSample):
traj = sampleTrajectory(policy)
trajReward = calcTrajRewardWithIndividualWolfReward(traj, wolvesID)
trajRewardList.append(trajReward)
meanTrajReward = np.mean(trajRewardList)
seTrajReward = np.std(trajRewardList) / np.sqrt(
len(trajRewardList) - 1)
print('meanTrajReward: ', meanTrajReward)
meanRewardListIndividWolf.append(meanTrajReward)
seListIndividWolf.append(seTrajReward)
print('meanRewardList: ', meanRewardList)
print('std error: ', seList)
print('meanRewardListIndividWolf: ', meanRewardListIndividWolf)
print('std error: ', seListIndividWolf)
plotResult = True
if plotResult:
fig = plt.figure()
plt.errorbar(numSheepsList, meanRewardList, seList)
plt.errorbar(numSheepsList, meanRewardListIndividWolf,
seListIndividWolf)
fig.suptitle('MADDPG Performance With 3 Wolves')
plt.xlabel('Number of Sheep')
plt.ylabel('Mean Episode Reward')
plt.xticks(numSheepsList)
plt.legend(['Shared reward', 'Individual reward'])
plt.show()
def main():
debug = 1
if debug:
numWolves = 3
numSheeps = 1
numBlocks = 2
timeconst = 0.5
dampratio = 0.2
saveTraj = False
visualizeTraj = True
maxTimeStep = 25
sheepSpeedMultiplier = 1.0
individualRewardWolf = 0
hasWalls = 1.5
dt = 0.05
visualizeMujoco = True
else:
print(sys.argv)
condition = json.loads(sys.argv[1])
numWolves = int(condition['numWolves'])
numSheeps = int(condition['numSheeps'])
numBlocks = int(condition['numBlocks'])
saveTraj = True
visualizeTraj = False
maxTimeStep = 50
sheepSpeedMultiplier = 1.25
individualRewardWolf = 1
print(
"maddpg: {} wolves, {} sheep, {} blocks, saveTraj: {}, visualize: {}".
format(numWolves, numSheeps, numBlocks, str(saveTraj),
str(visualizeTraj)))
numAgents = numWolves + numSheeps
numEntities = numAgents + numBlocks
wolvesID = list(range(numWolves))
sheepsID = list(range(numWolves, numAgents))
blocksID = list(range(numAgents, numEntities))
wolfSize = 0.075
sheepSize = 0.05
blockSize = 0.2
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheeps + [
blockSize
] * numBlocks
wolfMaxSpeed = 1.0
blockMaxSpeed = None
sheepMaxSpeedOriginal = 1.3
sheepMaxSpeed = sheepMaxSpeedOriginal * sheepSpeedMultiplier
entityMaxSpeedList = [wolfMaxSpeed] * numWolves + [
sheepMaxSpeed
] * numSheeps + [blockMaxSpeed] * numBlocks
entitiesMovableList = [True] * numAgents + [False] * numBlocks
massList = [1.0] * numEntities
isCollision = IsCollision(getPosFromAgentState)
punishForOutOfBound = PunishForOutOfBound()
rewardSheep = RewardSheep(wolvesID, sheepsID, entitiesSizeList,
getPosFromAgentState, isCollision,
punishForOutOfBound)
if individualRewardWolf:
rewardWolf = RewardWolfIndividual(wolvesID, sheepsID, entitiesSizeList,
isCollision)
else:
rewardWolf = RewardWolf(wolvesID, sheepsID, entitiesSizeList,
isCollision)
rewardFunc = lambda state, action, nextState: list(
rewardWolf(state, action, nextState)) + list(
rewardSheep(state, action, nextState))
dirName = os.path.dirname(__file__)
# physicsDynamicsPath=os.path.join(dirName,'..','..','environment','mujocoEnv','numBlocks=1_numSheeps=1_numWolves=1.xml')
if hasWalls:
# physicsDynamicsPath=os.path.join(dirName,'..','..','environment','mujocoEnv','hasWalls=1_numBlocks={}_numSheeps={}_numWolves={}timeconst={}dampratio={}.xml'.format(numBlocks,numSheeps,numWolves,timeconst,dampratio))
physicsDynamicsPath = os.path.join(
dirName, '..', '..', 'environment', 'mujocoEnv',
'dt={}'.format(dt),
'hasWalls={}_numBlocks={}_numSheeps={}_numWolves={}timeconst={}dampratio={}.xml'
.format(hasWalls, numBlocks, numSheeps, numWolves, timeconst,
dampratio))
# physicsDynamicsPath=os.path.join(dirName,'..','..','environment','mujocoEnv','crossTest3w1s2b.xml')
else:
physicsDynamicsPath = os.path.join(
dirName, '..', '..', 'environment', 'mujocoEnv',
'numBlocks={}_numSheeps={}_numWolves={}timeconst={}dampratio={}.xml'
.format(numBlocks, numSheeps, numWolves, timeconst, dampratio))
with open(physicsDynamicsPath) as f:
xml_string = f.read()
envXmlDict = xmltodict.parse(xml_string.strip())
envXml = xmltodict.unparse(envXmlDict)
physicsModel = mujoco.load_model_from_xml(envXml)
physicsSimulation = mujoco.MjSim(physicsModel)
print(physicsSimulation.model.body_pos)
# print(dir(physicsSimulation.model))
# print(dir(physicsSimulation.data),physicsSimulation.dataphysicsSimulation.data)
# print(physicsSimulation.data.qpos,dir(physicsSimulation.data.qpos))
# print(physicsSimulation.data.qpos,dir(physicsSimulation.data.qpos))
qPosInit = [0, 0] * numAgents
qVelInit = [0, 0] * numAgents
qVelInitNoise = 0 * hasWalls
qPosInitNoise = 0.8 * hasWalls
getBlockRandomPos = lambda: np.random.uniform(-0.7 * hasWalls, +0.7 *
hasWalls, 2)
getBlockSpeed = lambda: np.zeros(2)
numQPos = len(physicsSimulation.data.qpos)
numQVel = len(physicsSimulation.data.qvel)
sampleAgentsQPos = lambda: np.asarray(qPosInit) + np.random.uniform(
low=-qPosInitNoise, high=qPosInitNoise, size=numQPos)
sampleAgentsQVel = lambda: np.asarray(qVelInit) + np.random.uniform(
low=-qVelInitNoise, high=qVelInitNoise, size=numQVel)
minDistance = 0.2 + 2 * blockSize
isOverlap = IsOverlap(minDistance)
sampleBlockState = SampleBlockState(numBlocks, getBlockRandomPos,
getBlockSpeed, isOverlap)
reset = ResetUniformWithoutXPos(physicsSimulation, numAgents, numBlocks,
sampleAgentsQPos, sampleAgentsQVel,
sampleBlockState)
damping = 2.5
numSimulationFrames = int(0.1 / dt)
agentsMaxSpeedList = [wolfMaxSpeed] * numWolves + [sheepMaxSpeed
] * numSheeps
reshapeAction = ReshapeAction()
isTerminal = lambda state: False
transit = TransitionFunctionWithoutXPos(physicsSimulation, numAgents,
numSimulationFrames,
damping * dt * numSimulationFrames,
agentsMaxSpeedList,
visualizeMujoco, isTerminal,
reshapeAction)
maxRunningStepsToSample = 100
sampleTrajectory = SampleTrajectory(maxRunningStepsToSample, transit,
isTerminal, rewardFunc, reset)
observeOneAgent = lambda agentID: Observe(agentID, wolvesID, sheepsID,
blocksID, getPosFromAgentState,
getVelFromAgentState)
observe = lambda state: [
observeOneAgent(agentID)(state) for agentID in range(numAgents)
]
initObsForParams = observe(reset())
obsShape = [
initObsForParams[obsID].shape[0]
for obsID in range(len(initObsForParams))
]
worldDim = 2
actionDim = worldDim * 2 + 1
layerWidth = [128, 128]
# ------------ model ------------------------
buildMADDPGModels = BuildMADDPGModels(actionDim, numAgents, obsShape)
modelsList = [
buildMADDPGModels(layerWidth, agentID) for agentID in range(numAgents)
]
# individStr = 'individ' if individualRewardWolf else 'shared'
# fileName = "maddpg{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}{}_agent".format(
# numWolves, numSheeps, numBlocks, maxEpisode, maxTimeStep, sheepSpeedMultiplier, individStr)
# wolvesModelPaths = [os.path.join(dirName, '..', 'trainedModels', '3wolvesMaddpg', fileName + str(i) + '60000eps') for i in wolvesID]
# [restoreVariables(model, path) for model, path in zip(wolvesModel, wolvesModelPaths)]
#
# actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
# policy = lambda allAgentsStates: [actOneStepOneModel(model, observe(allAgentsStates)) for model in modelsList]
# modelPaths = [os.path.join(dirName, '..', 'trainedModels', '3wolvesMaddpg_ExpEpsLengthAndSheepSpeed', fileName + str(i)) for i in
# range(numAgents)]
# modelFolder = os.path.join(dirName, '..', 'trainedModels', 'mujocoMADDPG')
# modelFolder = os.path.join(dirName, '..', 'trainedModels', 'mujocoMADDPG','timeconst='+str(timeconst)+'dampratio='+str(dampratio))
if hasWalls:
# modelFolder = os.path.join(dirName, '..', 'trainedModels', 'mujocoMADDPG','hasWalls=1'+'numBlocks='+str(numBlocks)+'numSheeps='+str(numSheeps)+'numWolves='+str(numWolves)+'timeconst='+str(timeconst)+'dampratio='+str(dampratio))
# modelFolder = os.path.join(dirName, '..', 'trainedModels', 'mujocoMADDPG','dt={}'.format(dt),'hasWalls='+str(hasWalls)+'numBlocks='+str(numBlocks)+'numSheeps='+str(numSheeps)+'numWolves='+str(numWolves)+'timeconst='+str(timeconst)+'dampratio='+str(dampratio)+'individualRewardWolf='+str(individualRewardWolf)+'sheepSpeedMultiplier='+str(sheepSpeedMultiplier))
# modelFolder = os.path.join(dirName, '..', 'trainedModels', 'mujocoMADDPG','dt={}'.format(dt),'hasWalls='+str(hasWalls)+'numBlocks='+str(numBlocks)+'numSheeps='+str(numSheeps)+'numWolves='+str(numWolves)+'timeconst='+str(timeconst)+'dampratio='+str(dampratio)+'individualRewardWolf='+str(individualRewardWolf)+'sheepSpeedMultiplier='+str(sheepSpeedMultiplier))
modelFolder = os.path.join(
dirName, '..', 'trainedModels', 'mujocoMADDPGeavluateWall',
'dt={}'.format(dt),
'hasWalls=' + str(hasWalls) + 'numBlocks=' + str(numBlocks) +
'numSheeps=' + str(numSheeps) + 'numWolves=' + str(numWolves) +
'timeconst=' + str(timeconst) + 'dampratio=' + str(dampratio) +
'individualRewardWolf=' + str(individualRewardWolf) +
'sheepSpeedMultiplier=' + str(sheepSpeedMultiplier))
individStr = 'individ' if individualRewardWolf else 'shared'
fileName = "maddpghasWalls={}{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}{}_agent".format(
hasWalls, numWolves, numSheeps, numBlocks, maxEpisode, maxTimeStep,
sheepSpeedMultiplier, individStr)
else:
modelFolder = os.path.join(
dirName, '..', 'trainedModels', 'mujocoMADDPG',
'numBlocks=' + str(numBlocks) + 'numSheeps=' + str(numSheeps) +
'numWolves=' + str(numWolves) + 'timeconst=' + str(timeconst) +
'dampratio=' + str(dampratio))
fileName = "maddpg{}wolves{}sheep{}blocks60000episodes25stepSheepSpeed1.0shared_agent".format(
numWolves, numSheeps, numBlocks)
modelPaths = [
os.path.join(modelFolder, fileName + str(i) + '60000eps')
for i in range(numAgents)
]
[
restoreVariables(model, path)
for model, path in zip(modelsList, modelPaths)
]
actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
policy = lambda allAgentsStates: [
actOneStepOneModel(model, observe(allAgentsStates))
for model in modelsList
]
trajList = []
numTrajToSample = 50
for i in range(numTrajToSample):
np.random.seed(i)
traj = sampleTrajectory(policy)
trajList.append(list(traj))
# saveTraj
if saveTraj:
trajFileName = "maddpg{}wolves{}sheep{}blocks{}eps{}stepSheepSpeed{}{}Traj".format(
numWolves, numSheeps, numBlocks, maxEpisode, maxTimeStep,
sheepSpeedMultiplier, individStr)
trajSavePath = os.path.join(dirName, '..', 'trajectory', trajFileName)
saveToPickle(trajList, trajSavePath)
# visualize
if visualizeTraj:
entitiesColorList = [wolfColor] * numWolves + [
sheepColor
] * numSheeps + [blockColor] * numBlocks
render = Render(entitiesSizeList, entitiesColorList, numAgents,
getPosFromAgentState)
print(trajList[0][0], '!!!3', trajList[0][1])
trajToRender = np.concatenate(trajList)
render(trajToRender)
def simuliateOneParameter(parameterOneCondiiton, evalNum, randomSeed):
saveTraj = True
visualizeTraj = False
visualizeMujoco = False
numWolves = parameterOneCondiiton['numWolves']
numSheeps = parameterOneCondiiton['numSheeps']
numBlocks = parameterOneCondiiton['numBlocks']
timeconst = parameterOneCondiiton['timeconst']
dampratio = parameterOneCondiiton['dampratio']
maxTimeStep = parameterOneCondiiton['maxTimeStep']
sheepSpeedMultiplier = parameterOneCondiiton['sheepSpeedMultiplier']
individualRewardWolf = parameterOneCondiiton['individualRewardWolf']
hasWalls = parameterOneCondiiton['hasWalls']
dt = parameterOneCondiiton['dt']
print(
"maddpg: {} wolves, {} sheep, {} blocks, saveTraj: {}, visualize: {}".
format(numWolves, numSheeps, numBlocks, str(saveTraj),
str(visualizeTraj)))
numAgents = numWolves + numSheeps
numEntities = numAgents + numBlocks
wolvesID = list(range(numWolves))
sheepsID = list(range(numWolves, numAgents))
blocksID = list(range(numAgents, numEntities))
wolfSize = 0.075
sheepSize = 0.05
blockSize = 0.2
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheeps + [
blockSize
] * numBlocks
wolfMaxSpeed = 1.0
blockMaxSpeed = None
sheepMaxSpeedOriginal = 1.3
sheepMaxSpeed = sheepMaxSpeedOriginal * sheepSpeedMultiplier
entityMaxSpeedList = [wolfMaxSpeed] * numWolves + [
sheepMaxSpeed
] * numSheeps + [blockMaxSpeed] * numBlocks
entitiesMovableList = [True] * numAgents + [False] * numBlocks
massList = [1.0] * numEntities
isCollision = IsCollision(getPosFromAgentState)
punishForOutOfBound = PunishForOutOfBound()
rewardSheep = RewardSheep(wolvesID, sheepsID, entitiesSizeList,
getPosFromAgentState, isCollision,
punishForOutOfBound)
if individualRewardWolf:
rewardWolf = RewardWolfIndividual(wolvesID, sheepsID, entitiesSizeList,
isCollision)
else:
rewardWolf = RewardWolf(wolvesID, sheepsID, entitiesSizeList,
isCollision)
rewardFunc = lambda state, action, nextState: list(
rewardWolf(state, action, nextState)) + list(
rewardSheep(state, action, nextState))
dirName = os.path.dirname(__file__)
if hasWalls:
physicsDynamicsPath = os.path.join(
dirName, '..', '..', 'environment', 'mujocoEnv',
'dt={}'.format(dt),
'hasWalls={}_numBlocks={}_numSheeps={}_numWolves={}timeconst={}dampratio={}.xml'
.format(hasWalls, numBlocks, numSheeps, numWolves, timeconst,
dampratio))
else:
physicsDynamicsPath = os.path.join(
dirName, '..', '..', 'environment', 'mujocoEnv',
'numBlocks={}_numSheeps={}_numWolves={}timeconst={}dampratio={}.xml'
.format(numBlocks, numSheeps, numWolves, timeconst, dampratio))
with open(physicsDynamicsPath) as f:
xml_string = f.read()
envXmlDict = xmltodict.parse(xml_string.strip())
envXml = xmltodict.unparse(envXmlDict)
physicsModel = mujoco.load_model_from_xml(envXml)
physicsSimulation = mujoco.MjSim(physicsModel)
# print(physicsSimulation.model.body_pos)
qPosInit = [0, 0] * numAgents
qVelInit = [0, 0] * numAgents
qVelInitNoise = 0 * hasWalls
qPosInitNoise = 0.8 * hasWalls
getBlockRandomPos = lambda: np.random.uniform(-0.7 * hasWalls, +0.7 *
hasWalls, 2)
getBlockSpeed = lambda: np.zeros(2)
numQPos = len(physicsSimulation.data.qpos)
numQVel = len(physicsSimulation.data.qvel)
sampleAgentsQPos = lambda: np.asarray(qPosInit) + np.random.uniform(
low=-qPosInitNoise, high=qPosInitNoise, size=numQPos)
sampleAgentsQVel = lambda: np.asarray(qVelInit) + np.random.uniform(
low=-qVelInitNoise, high=qVelInitNoise, size=numQVel)
minDistance = 0.2 + 2 * blockSize
isOverlap = IsOverlap(minDistance)
sampleBlockState = SampleBlockState(numBlocks, getBlockRandomPos,
getBlockSpeed, isOverlap)
reset = ResetUniformWithoutXPos(physicsSimulation, numAgents, numBlocks,
sampleAgentsQPos, sampleAgentsQVel,
sampleBlockState)
damping = 2.5
numSimulationFrames = int(0.1 / dt)
agentsMaxSpeedList = [wolfMaxSpeed] * numWolves + [sheepMaxSpeed
] * numSheeps
reshapeAction = ReshapeAction()
isTerminal = lambda state: False
transit = TransitionFunctionWithoutXPos(physicsSimulation, numAgents,
numSimulationFrames,
damping * dt * numSimulationFrames,
agentsMaxSpeedList,
visualizeMujoco, isTerminal,
reshapeAction)
maxRunningStepsToSample = 100
sampleTrajectory = SampleTrajectory(maxRunningStepsToSample, transit,
isTerminal, rewardFunc, reset)
observeOneAgent = lambda agentID: Observe(agentID, wolvesID, sheepsID,
blocksID, getPosFromAgentState,
getVelFromAgentState)
observe = lambda state: [
observeOneAgent(agentID)(state) for agentID in range(numAgents)
]
initObsForParams = observe(reset())
obsShape = [
initObsForParams[obsID].shape[0]
for obsID in range(len(initObsForParams))
]
worldDim = 2
actionDim = worldDim * 2 + 1
layerWidth = [128, 128]
# ------------ model ------------------------
buildMADDPGModels = BuildMADDPGModels(actionDim, numAgents, obsShape)
modelsList = [
buildMADDPGModels(layerWidth, agentID) for agentID in range(numAgents)
]
if hasWalls:
modelFolder = os.path.join(
dirName, '..', 'trainedModels', 'mujocoMADDPGeavluateWall',
'dt={}'.format(dt),
'hasWalls=' + str(hasWalls) + 'numBlocks=' + str(numBlocks) +
'numSheeps=' + str(numSheeps) + 'numWolves=' + str(numWolves) +
'timeconst=' + str(timeconst) + 'dampratio=' + str(dampratio) +
'individualRewardWolf=' + str(individualRewardWolf) +
'sheepSpeedMultiplier=' + str(sheepSpeedMultiplier))
individStr = 'individ' if individualRewardWolf else 'shared'
fileName = "maddpghasWalls={}{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}{}_agent".format(
hasWalls, numWolves, numSheeps, numBlocks, maxEpisode, maxTimeStep,
sheepSpeedMultiplier, individStr)
else:
modelFolder = os.path.join(
dirName, '..', 'trainedModels', 'mujocoMADDPG',
'numBlocks=' + str(numBlocks) + 'numSheeps=' + str(numSheeps) +
'numWolves=' + str(numWolves) + 'timeconst=' + str(timeconst) +
'dampratio=' + str(dampratio))
fileName = "maddpg{}wolves{}sheep{}blocks60000episodes25stepSheepSpeed1.0shared_agent".format(
numWolves, numSheeps, numBlocks)
modelPaths = [
os.path.join(modelFolder, fileName + str(i) + '60000eps')
for i in range(numAgents)
]
[
restoreVariables(model, path)
for model, path in zip(modelsList, modelPaths)
]
actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
policy = lambda allAgentsStates: [
actOneStepOneModel(model, observe(allAgentsStates))
for model in modelsList
]
startTime = time.time()
trajList = []
for i in range(evalNum):
np.random.seed(i)
traj = sampleTrajectory(policy)
trajList.append(list(traj))
endTime = time.time()
print("Time taken {} seconds to generate {} trajectories".format(
(endTime - startTime), evalNum))
# saveTraj
if saveTraj:
trajectoryFolder = os.path.join(dirName, '..', 'trajectory',
'evluateWall')
if not os.path.exists(trajectoryFolder):
os.makedirs(trajectoryFolder)
trajectorySaveExtension = '.pickle'
fixedParameters = {'randomSeed': randomSeed, 'evalNum': evalNum}
generateTrajectorySavePath = GetSavePath(trajectoryFolder,
trajectorySaveExtension,
fixedParameters)
trajectorySavePath = generateTrajectorySavePath(parameterOneCondiiton)
saveToPickle(trajList, trajectorySavePath)
# visualize
if visualizeTraj:
entitiesColorList = [wolfColor] * numWolves + [
sheepColor
] * numSheeps + [blockColor] * numBlocks
render = Render(entitiesSizeList, entitiesColorList, numAgents,
getPosFromAgentState)
print(trajList[0][0], '!!!3', trajList[0][1])
trajToRender = np.concatenate(trajList)
render(trajToRender)
return endTime - startTime
